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SAEHD: added option Enable random warp of samples, default is on

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Random warp is required to generalize facial expressions of both faces. When the face is trained enough, you can disable it to get extra sharpness for less amount of iterations.
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Colombo 2019-10-12 10:31:50 +04:00
parent e15f846d08
commit 92f14dee70
4 changed files with 33 additions and 29 deletions
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20
nnlib/nnlib.py
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@ -631,26 +631,8 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
reduction_axes = list(range(len(input_shape)))
del reduction_axes[self.axis]
#broadcast_shape = [1] * len(input_shape)
#broadcast_shape[self.axis] = input_shape[self.axis]
#normed = x# (x - K.reshape(self.moving_mean,broadcast_shape) ) / ( K.sqrt( K.reshape(self.moving_variance,broadcast_shape)) +self.epsilon)
#normed *= K.reshape(gamma,[-1]+broadcast_shape[1:] )
#normed += K.reshape(beta, [-1]+broadcast_shape[1:] )
#mean = K.mean(x, axis=reduction_axes)
#self.moving_mean = self.add_weight(shape=(units,), name='moving_mean', initializer='zeros',trainable=False)
#self.moving_variance = self.add_weight(shape=(units,), name='moving_variance',initializer='ones', trainable=False)
#variance = K.var(x, axis=reduction_axes)
#sample_size = K.prod([ K.shape(x)[axis] for axis in reduction_axes ])
#sample_size = K.cast(sample_size, dtype=K.dtype(x))
#variance *= sample_size / (sample_size - (1.0 + self.epsilon))
#self.add_update([K.moving_average_update(self.moving_mean, mean, self.momentum),
# K.moving_average_update(self.moving_variance, variance, self.momentum)], None)
#return normed
del reduction_axes[0]
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis]
mean = K.mean(x, reduction_axes, keepdims=True)